Bacterial colony foraging for multi-mode product colour planning

Abstract

Abstract:In this work, in order to assist designer in colour planning during product development, an efficient synthesised evaluation model is presented to evaluate colour-combination schemes of multi-working modes products (MMP). A novel bacterial colony foraging (BCF) algorithm is proposed to search for the optimal colour-combination schemes of MMP based on the evaluation model. The proposed BCF extend original bacterial foraging algorithm to adaptive and cooperative mode by combining bacterial chemotaxis, cell-to-cell communication, and a self-adaptive foraging strategy. The experiment presents an exhaustive comparison of the proposed BCF and two successful bio-inspired search techniques, namely the genetic algorithm (GA) and particle swarm optimisation (PSO), on three MMP tested cases of different nature, namely a hair-drier with two-coloured areas and two working modes, and two arm-type aerial work platforms both two-coloured products while with two and three working modes, respectively. Simulation results demonstrate that the proposed method is feasible and efficient.

A new monarch butterfly optimization with an improved crossover operator

“…Since genetic algorithms (GAs) (Goldberg 1998;Gao and Ovaska 2002;Amirjanov and Sobolev 2015) are proposed in the 1960s, various metaheuristic algorithms are put forward and used to successfully address many complicated engineering problems, such as scheduling (Hu et al 2011;Karthikeyan et al 2015;Gopinadh and Singh 2015), test-sheet composition (Duan et al 2012), target assessment (Wang et al 2012b(Wang et al , 2013b, path planning (Wang et al 2012c, d, e), directing orbits of chaotic systems (Cui et al 2013), product color planning (Chen et al 2015b), parameter identification (Rashidi et al 2015), task assignment problem (Zou et al 2010a(Zou et al , 2011c, factor evaluation (Wang et al 2012b(Wang et al , 2013a, feature selection , wind generator optimization (Gao et al 2012), reliability problems (Zou et al 2010b(Zou et al , 2011a, knapsack problem (Zou et al 2011b;Feng et al 2015), and fault diagnosis (Yi et al 2016). Recently, several effective SI methods have been proposed, such as ant colony optimization (ACO) (Dorigo et al 1996;Martinovic ´and Bajer 2015), particle swarm optimization (PSO) (Kennedy and Eberhart 1995;Mirjalili et al 2014c;Wang et al 2014e, 2016bZhao et al 2012;Zhao 2010;Grillo et al 2015), artificial bee colony (ABC) (Karaboga and Basturk 2007;Li and Yin 2012), cuckoo search (CS) (Yang and Deb 2009;Wang et al 2012a, bat algorithm (BA) (Yang 2010a;…”

A Novel Hybrid Cuckoo Search Algorithm with Global Harmony Search for 0–1 Knapsack Problems

“…Some of them include ant colony optimization (ACO) 1 , bat algorithm (BA) 2 , bacterial colony foraging (BCF) 3 , bird swarm algorithm (BSA) 4 , chicken swarm optimization (CSO) 5 , differential evolution (DE) [6][7] , firefly algorithm (FA) [8][9][10] , krill herd algorithm (KH) [11][12][13][14][15][16][17][18] , monarch butterfly optimization (MBO) [19][20][21] , particle swarm optimization (PSO) [22][23] , earthworm optimization algorithm (EWA) 24 and elephant herding optimization (EHO) [25][26] . These algorithms have been used to successfully address many complicated engineering problems, such as ordinal regression 27 , classification 28 , data encryption 29 , possession 30 , scheduling 31 , test-sheet composition 32 , target assessment [33][34] , path planning [35][36][37] , directing orbits of chaotic systems 38 , feature selection 39 , and fault diagnosis 40 .…”